MALES

Machine Learning for Single Star Scidar

Optical satellite communications is a growing research field with bright commercial perspectives. One of the challenges for optical links through the atmosphere is turbulence, which is also apparent by the twinkling of stars. The reduction of the quality can be calculated, but it needs the turbulence strength over the path the optical beam is running. Estimation of the turbulence strength is done at astronomic sites, but not at rural or urban sites. To be able to do this, a simple instrument is required. We want to propose to use a single star Scintillation Detection and Ranging (SCIDAR), which is an instrument that can estimate the turbulence strength, based on the observation of a single star. Here, reliable signal processing of the received images of the star is most challenging. We propose to solve this by Machine Learning.

Participating organisations

Delft University of Technology
Netherlands eScience Center
Natural Sciences & Engineering
Natural Sciences & Engineering

Team

RS
Rudolf Saathof
Lead Applicant
Delft University of Technology
Simone Ciarella
eScience Research Engineer
Netherlands eScience Center
Rena Bakhshi
Rena Bakhshi
Programme Manager
Netherlands eScience Center
MA
Marguerite Arvis
MP
Maximilian Pierzyna
PhD student
Technische Universiteit Delft

Related software

speckcn2

SP

SpeckleCn2Profiler revolutionizes satellite communication, combining SCIDAR and AI. By accurately estimating turbulence strength and refining signals, it ensures reliable performance in any environment. Dive into our repository and join the quest to transform optical links.

Updated 5 months ago